Some organizations monitor the likelihood that they will endure losses resulting from risks associated with their operations. Such operational risks may be modeled by the organizations in order to comply with regulatory requirements, to improve capital allocation, for benchmarking, or to improve processes. One issue with such modeling is that an individual organization may not have loss data suitable to produce a very accurate operational risk model.
To overcome the problem of individual organizations' not having suitable loss data, organizations have joined together into consortia, which pool the loss data from their constituent organizations. These consortia typically have combined the loss data from the constituent organizations and whitewashed the data, to remove from the pooled data all information that potentially could identify the source of the loss data. This single set of combined, whitewashed data then was returned to the constituent organizations, which used the returned data sets in their own operational risk modeling processes.
The use of pooled, whitewashed data from a consortium of organizations is not, however, an optimal solution to the problem of organizations' lack of access to suitable loss data. The process of whitewashing the data removes from the data set relevant information that could significantly improve the usefulness of the data set to organizations' operational risk modeling by improving the predictive power of the generated models. Individual organizations, though, are typically more concerned with maintaining the confidentiality of their internal data than with improving the predictive power of their operational risk modeling. Thus, there is a need for a way to produce operational risk models with more predictive power for organizations without compromising the confidentiality of the organizations' data.